“We will be driven not by mere hope or economic necessity. We are going to be driven by the science, the data and public health.”
So said the Prime Minister on 10 May as he prepared the country for the gradual release from the grip of the Covid-19 lockdown. Before Covid, Ministers, when interrogated on one policy or another, would state that they sought to look at things ‘in the round’, that awful cliché meaning that different priorities would be weighed up and a balanced decision taken. The Prime Minister’s words suggest that as the virus continues its assault on the health of the world’s citizens, government thinking on issues to do with public health will be less round, and more square-shaped.
The hard edges of policy development are made possible by a new obsession with, and reliance on science, data and forecasting. This obsession was cultivated before the Covid crisis, when Boris Johnson and his trusty steed Dominic Cummings moved into Number 10 in July 2019. In doing so, they followed a practice that has been commonplace in business for decades, where commercial decisions are routinely taken following a rigorous analytical process based on data.
Data on its own is about as useful as a button on a sock. It’s a raw material that needs value added – intelligent interpretation – which turns it into actionable insight. Out of that comes a consensus view of what story the data tells and on which decisions can then be based. This process provides zero guarantee that the indisputable truth has been found, but it does help to spread the risk of getting decisions wrong between more than one person. During the Covid crisis, the R-rate is derived following a similar process.
To call the output of interpreting data a piece of science can mislead, as it suggests that its conclusions are beyond challenge. If that were so, then all commercial product launches would be guaranteed success stories and Prof. Neil Ferguson’s Covid-19 forecasting model would have been automatically right. His data was undoubtedly valid, the problem was that the model used to interpret the data was shown to be flawed.
The contributions of public health leaders to the fight against Covid-19 have been significant. They have stepped up to the plate and taken responsibility when the nation needed them. Before Covid, their attention in preventing disease in the population was mostly focused on so-called ‘non-communicable disease’ or illness brought about by lifestyle choices (alcohol use, bad diet, lack of exercise, smoking). To prove the case for state intervention in lifestyles, campaigners rely on data collection, analysis and forecasting – a case that is then often presented as indisputable scientific truth. This lobbying method has convinced UK governments of the need to introduce a sugar tax on soft drinks, minimum alcohol pricing and cigarette plain packaging. Chris Snowdon, Head of Lifestyle Economics at the Institute of Economic Affairs has long been tracking the quality of the ‘science’ used by public health campaigners and his blog is revealing.
In a post-lockdown world, the threat of communicable disease like Covid-19 will undoubtedly remain prominent on the ‘must sort out’ wallchart. Only a vaccine or effective treatment available to all citizens can diminish that priority. Unless or until proper scientific advances are made, focus will remain on helping the population be more resilient to withstand Covid-19 and other viruses and diseases.
The Prime Minister himself has acknowledged that his being overweight hindered him in his recovery from the virus and is said to be determined to do something about it. Given his position, there is logic in him wanting to try to protect others from a similar disadvantage and look for ways to help people become more fit and resilient.
The public health community by now has the entire Cabinet on proverbial speed dial and will be only too ready to propose its regulatory solutions backed up with copious amounts of data, analysis and forecasting accumulated over the years.
Businesses concerned that their products might risk getting knocked down on this public health data superhighway may be working on their response scenarios for future regulatory pressure. As they prepare to send a torrent of data traffic they should be undeterred by attempts to discredit their data as being ‘self-serving’ and therefore unscientific and unreliable.